Calibration and prediction procedure of rock avalanche through advancing numerical simulation

Rock avalanches are large volume landslides composed of flowing fragments of rock that can reach velocities in excess of 50 m/s, impact large areas, and can seriously threaten the safety of people and infrastructure. Numerical models play a crucial role in forecasting the hazard and risk associated with rock avalanches. The Orin3D model, based on the equivalent fluid concept, can be used to simulate rock avalanche motion, however it is unknown what the best model parameterization is for forecasting.  However, Orin3D is implemented to run on a graphical processing unit (GPU), which improves simulation times by two orders of magnitude, making large-scale calibration feasible, as is investigated herein. 
In the present work, we use a posterior analysis based on Bayesian statistics to calibrate Orin3D for three different parameterizations: 1) Frictional rheology, 2) Voellmy rheology, and 3) the combination of Frictional and Voellmy rheology, using a data set containing 22 historical rock avalanche cases, and requiring over 450,000 model runs. Based on the calibration results, a probabilistic prediction framework is then tested that generates pseudo-predictions for the cases in the database, incorporating key features of rock avalanches, such as path materials and topographic constraints. We find that, among these three rheological settings, the best prediction results for most cases are obtained with the combination of Frictional and Voellmy rheology. We further use these results to suggest a prediction procedure that considers the volume, path material and topographic confinements of rock avalanches, which provide guidance for the rheological setting in the model and important basis for the prediction and mitigation of rock avalanche hazards in practice.